Month: July 2024

Abstract

Connectivity maps are now available for the 360 cortical regions in the Human Connectome Project Multimodal Parcellation atlas. Here we add function to these maps by measuring selective fMRI activations and functional connectivity increases to stationary visual stimuli of faces, scenes, body parts and tools from 956 HCP participants. Faces activate regions in the ventrolateral visual cortical stream (FFC), in the superior temporal sulcus (STS) visual stream for face and head motion; and inferior parietal visual (PGi) and somatosensory (PF) regions. Scenes activate ventromedial visual stream VMV and PHA regions in the parahippocampal scene area; medial (7m) and lateral parietal (PGp) regions; and the reward-related medial orbitofrontal cortex. Body parts activate the inferior temporal cortex object regions (TE1p, TE2p); but also visual motion regions (MT, MST, FST); and the inferior parietal visual (PGi, PGs) and somatosensory (PF) regions; and the unpleasant-related lateral orbitofrontal cortex. Tools activate an intermediate ventral stream area (VMV3, VVC, PHA3); visual motion regions (FST); somatosensory (1, 2); and auditory (A4, A5) cortical regions. The findings add function to cortical connectivity maps; and show how stationary visual stimuli activate other cortical regions related to their associations, including visual motion, somatosensory, auditory, semantic, and orbitofrontal cortex value-related, regions.

Abstract

Advanced persistent threats (APTs) present a significant cybersecurity challenge, necessitating innovative detection methods. This study stands out by integrating advanced data preparation with strategies for handling data imbalances, tailored for the SCVIC-APT-2021 dataset. We employ a mix of resampling, cost-sensitive learning, and ensemble methods, alongside machine learning and deep learning models like XGBoost, LightGBM, and ANNs, to enhance APT detection. Our strategy, which draws from the MITRE ATT&CK framework, concentrates on each stage of APT attacks, which significantly increases detection accuracy. Notably, we achieved a Macro F1-score of 95.20% with XGBoost and 96.67% with LightGBM, and significant enhancements in the area under the precision–recall curve for both. Our study’s exploration of the SCVIC-APT-2021 dataset marks a progressive step in APT detection research, with vital implications for future cybersecurity developments.

Abstract

Lung cancer is an uncontrolled growth of tissue causing a lump in the human lung. If lung cancer can be detected early, it can increase the survival rate. Therefore, a multi-classification approach of lung nodule detection with high computational effectiveness is required. In this paper, a multi-classification approach of lung nodule detection and classification is proposed using artificial intelligence on computed tomography (CT) scan images. Different preprocessing steps are applied for resizing, smoothing, and enhancement of the CT images. Then, two different approaches for feature extraction using VGG16 transfer learning and morphological segmentation are proposed. Morphological segmentation and feature extraction are applied for the segmentation of the region of interest and to extract the distinct features. Finally, the proposed deep learning architecture and seven different machine learning algorithms are applied on the preprocessed data and the extracted features for the classification of lung nodules into three classes: malignant, benign, and normal. It is observed that the stacked ensemble model of deep learning convolutional neural network (CNN) and VGG16 transfer learning models (CNN+VGG16) can achieve 99.55% accuracy using preprocessed data. It is also observed that all the ML algorithms perform with reasonably high accuracy using the low-dimensional morphological features. It is observed from the fivefold cross-validation results that logistic regression performs with 99.36% accuracy in 23.71 s time using the preprocessed data. Whereas, using the morphological features, k-nearest neighbor, and the support vector machine perform with the highest accuracy of 99.76% with very reduced computational time of 0.017 and 0.008 s, respectively.

Abstract

Background

Oncology outreach is a common strategy for increasing rural access to cancer care, where traveling oncologists commute across healthcare settings to extend specialized care. Examining the extent to which physician outreach is associated with timely treatment for rural patients is critical for informing outreach strategies.

Methods

We identified a 100% fee-for-service sample of incident breast cancer patients from 2015 to 2020 Medicare claims and apportioned them into surgery and adjuvant therapy cohorts based on treatment history. We defined an outreach visit as the provision of care by a traveling oncologist at a clinic outside of their primary hospital service area. We used hierarchical logistic regression to examine the associations between patient receipt of preoperative care at an outreach visit (preoperative outreach) and > 60-day surgical delay, and patient receipt of postoperative care at an outreach visit (postoperative outreach) and > 60-day adjuvant delay.

Results

We identified 30,337 rural-residing patients who received breast cancer surgery, of whom 4071 (13.4%) experienced surgical delay. Among surgical patients, 14,501 received adjuvant therapy, of whom 2943 (20.3%) experienced adjuvant delay. In adjusted analysis, we found that patient receipt of preoperative outreach was associated with reduced odds of surgical delay (odds ratio [OR] 0.75, 95% confidence interval [CI] 0.61–0.91); however, we found no association between patient receipt of postoperative outreach and adjuvant delay (OR 1.04, 95% CI 0.85–1.25).

Conclusions

Our findings indicate that preoperative outreach is protective against surgical delay. The traveling oncologists who enable such outreach may play an integral role in catalyzing the coordination and timeliness of patient-centered care.

Abstract

Pakistan is exposed to variety of hazards. Drought is one of the hydro-meteorological hazards causing loss of life and soil moisture, reduction in agricultural production and decline in groundwater level. Balochistan is the largest province of Pakistan which has been severely affected by drought. This study is an effort to analyze and forecast the spatial pattern of drought in Balochistan using Markov Model. Both primary and secondary data were utilized in Geographical Information System (GIS) environment to model the current and predict the future pattern of drought in the region. The main input spatial layers included land cover, natural difference vegetation index (NDVI), natural difference water index (NDWI), groundwater level, precipitation and current drought. The spatial pattern of drought is delineated into extreme, moderate and low/no drought zones. The analysis reveals that the currently, the extreme, moderate and low/no drought zones are spatially extended over 9.9% (37,053 km²), 83.11% (288,553 km²) and 6.21% (21,584 km²), respectively. While the predicted drought presents shocking results, where the prevalence of extreme, moderate and low/no drought zones may spatially extend over to 51.14% (191,364 km²),28.78% (99,945 km²) and 16.09 (55,881 km²). The extreme drought may encroach current moderate drought into predicted extreme drought. Additionally, the occurrence of drought in Chaghi district may be more in future, where chances of extreme drought occurrence is 20.15%. Second most prone region is Kharan, where chances of drought are 11.35% and finally, third region is Khuzdar, where the probability of extreme drought is 8.90%. However, Awaran, Panjgur, Kech, Zhob and Sherani districts are also prone to moderate drought in future. There is dire need to build mini and micro dams to conserve rain water and farmers should plant less water dependent crops. The results and findings of this study have potential to assist disaster management authorities and decision makers to formulate zone specific drought risk reduction strategies.

Abstract

Discrete gradients (DG) or more exactly discrete gradient methods are time integration schemes that are custom-built to preserve first integrals or Lyapunov functions of a given ordinary differential equation (ODE). In conservative molecular dynamics (MD) simulations, the energy of the system is constant and therefore a first integral of motion. Hence, discrete gradient methods seem to be a natural choice as an integration scheme in conservative molecular dynamics simulations.